code: def test_timegapsplit_with_gridsearch():
      cv = TimeGapSplit(
          date_serie=df["date"],
          train_duration=timedelta(days=5),
          valid_duration=timedelta(days=3),
          gap_duration=timedelta(days=0),
      )
  
      Lasso(random_state=0, tol=0.1, alpha=0.8).fit(X_train, y_train)
  
      pipe = Pipeline([("reg", Lasso(random_state=0, tol=0.1))])
      alphas = [0.1, 0.5, 0.8]
      grid = GridSearchCV(pipe, {"reg__alpha": alphas}, cv=cv)
      grid.fit(X_train, y_train)
      best_C = grid.best_estimator_.get_params()["reg__alpha"]
  
      assert best_C
  
file_path: scikit-lego/tests/test_model_selection/test_timegapsplit.py
imports: import datetime
  from datetime import timedelta
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import Lasso
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import Pipeline
  from sklego.model_selection import TimeGapSplit
label_desc: Use image node_id 8 for calling a global function with example usage: test_timegapsplit_with_gridsearch() without return types